Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …

Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

Importance The ENIGMA clinical high risk (CHR) for psychosis initiative, the largest pooled
neuroimaging sample of individuals at CHR to date, aims to discover robust neurobiological …

Age has a major effect on brain volume. However, the normative studies available are
constrained by small sample sizes, restricted age coverage and significant methodological …

The recent advancements in artificial intelligence (AI) combined with the extensive amount
of data generated by today's clinical systems, has led to the development of imaging AI …

Multisite magnetic resonance imaging (MRI) is increasingly used in clinical research and
development. Measurement biases—caused by site differences in scanner/image …

Background Establishing objective and quantitative neuroimaging biomarkers at individual
level can assist in early and accurate diagnosis of major depressive disorder (MDD) …

Schizophrenia is associated with widespread alterations in subcortical brain structure. While
analytic methods have enabled more detailed morphometric characterization, findings are …

Federated learning has shown its unique advantages in many different tasks, including brain
image analysis. It provides a new way to train deep learning models while protecting the …

Site differences, or systematic differences in feature distributions across multiple data-
acquisition sites, are a known source of heterogeneity that may adversely affect large-scale …